Download presentation

Presentation is loading. Please wait.

Published byOlivia Roper Modified over 3 years ago

1
Looking at Problems the Other Way Round: Engineering Applications of Inverse Simulation Based on Continuous System Simulation Methods David J. Murray-Smith, Emeritus Professor and Honorary Senior Research Fellow, School of Engineering, Rankine Building University of Glasgow, Glasgow G12 8QQ, Scotland, U.K. E-mail: David.Murray-Smith@glasgow.ac.uk Keynote Lecture: The inverse simulation approach

2
ORGANISATION Part 1: Why use an inverse simulation approach? Areas where it has proved useful. E.g. helicopter flight dynamics; land vehicles; surface ships and underwater vehicles Part 2: A brief introduction to inverse simulation methods. Iterative methods; methods based on continuous system simulation tools. Part 3: Inverse simulation based on continuous system simulation methods. Methods based on feedback principles; other approaches. Part 4: Experience. Multi-input multi-output systems; applications involving actuator dynamics as in fixed-wing aircraft, helicopters and underwater vehicles. Part 5: Discussion and conclusions

3
. Keynote Lecture : The inverse simulation approach Part 1: Why use an inverse simulation approach? What do we mean by “inverse simulation” and how can it be used?

4
. Keynote Lecture: The inverse simulation approach What is Inverse Simulation? Conventional modelling and simulation: a process of finding a model “output” for a given set of initial conditions and a prescribed time history of “inputs”. Inverse modelling and simulation: a process through which “inputs” are found that will produce a prescribed model “output”.

5
I Inputs: rudder, stern planes, top and bottom bow-planes, port and starboard bow-planes, propeller. Limits: 20 degrees for control surfaces; 1500 rpm for propeller. ( From Healey, A. J. and Lienard, D., Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles, IEEE J. Ocean Engineering, Vol.18, No. 3, pp. 327-339, 1993) Example: An Unmanned Underwater Vehicle (UUV) control surfaces shown Keynote Lecture: The inverse simulation approach

6
. Essentials of the inverse approach Use of inverse models or inverse simulations changes how we look at a problem and can provide insight that is not so readily available from forward simulation. Emphasises the control action to achieve a given output. Has been used especially in systems with a human operator to investigate levels of difficulty for specific tasks and to establish operator limits. Useful also for external validation, especially where measured responses involve a drift component due to inherent integral action within the system under test. Again, provides different kinds of physical insight from forward simulation.

7
Initial value problem: Conventional simulation: Inverse simulation: model output Inverse model desired outputInput needed Keynote Lecture: The inverse simulation approach

8
Some of first applications involved aircraft and helicopter manoeuvrability and handling qualities investigations. For example: Approach is also appropriate for many other application areas where actuators may reach limits in terms of amplitudes or rates. E.g, the underwater vehicle case. Keynote Lecture: The inverse simulation approach

9
. Take-off procedures from an offshore platform to ensure that a helicopter can recover following a failure of one engine in the second phase of climb. Keynote Lecture: The inverse simulation approach Recovery procedures and pilot work-load studies Both diagrams from Thomson, D. and Bradley, R., Inverse simulation as a tool for flight dynamics research-Principles and applications, Progress in Aerospace Sciences, 42, 174-210, 2006.

10
. Another example: Investigation of the potential performance of a helicopter in the early stages of design First must choose series of manoeuvres appropriate to the proposed role of the helicopter. For a given helicopter model, inverse simulation can then help answer questions about performance limits, performance sensitivities and possible design changes. Applications in conceptual design Keynote Lecture: The inverse simulation approach

11
Can the helicopter with known power and control limits fly the manoeuvre without exceeding vehicle limits? If the answer is NO then can consider What changes of design can allow requirements to be met? If the answer is YES then can consider issues such as: What is the “margin” of control that the pilot will have? What are the mass and centre of gravity limitations for each manoeuvre? K eynote Lecture: The inverse simulation approach

12
From: Cameron, N.,Thomson, D.G. and Murray-Smith, D.J., ‘Pilot modelling and inverse simulation for initial handling qualities assessment’, Aeronautical J., 107, 511-520, (2003). Slalom manoeuvre Keynote Lecture: The inverse simulation approach

13
. Left: Collective input; Right: Lateral cyclic input From: Thomson and Bradley, Proc. ERF, (1990) Control inputs for a slalom manoeuvre From: Thomson, D.G. and Bradley, R. ‘The use of inverse simulation for conceptual design’ Proc. European Rotorcraft Forum, Glasgow, UK, 16-18 September 1990, Royal Aeronautical Society, London, UK (1990).

14
Keynote Lecture: The inverse simulation approach From: Thomson, D.G. and Bradley, R. ‘The use of inverse simulation for conceptual design’ Proc. European Rotorcraft Forum, Glasgow, UK, 16-18 September 1990, Royal Aeronautical Society, London, UK (1990). Lateral cyclic input for a slalom manoeuvre: second and third attempts

15
Inverse Simulation for Model ValidationInverse Simulation for Model Validation Involves using measured response data from experiments on real system as input data for an inverse simulation based on the available model. The difference between known experimental input and input obtained from the inverse simulation algorithm may provide insight not readily available from comparisons of system and model outputs from forward simulation runs (e.g when drift in experimental response data is an issue). Particularly useful in pointing to structural inadequacies in a model. Work relates mainly to helicopter flight mechanics modelling. External validation of models through inverse simulation Keynote Lecture: The inverse simulation approach

16
Inverse Simulation in Integrated System DesignInverse Simulation in Integrated System Design Particularly appropriate where measured responses can be affected by offsets and resultant drift effects. Inverse simulation within the external validation process 1/(s+1) 1/s offset + + + - Inverse Model Keynote Lecture: The inverse simulation approach

17
Inverse Simulation in Integrated System DesignInverse Simulation in Integrated System Design Inverse simulation within the external validation process 1/(s+1) 1/s offset + + + - Keynote Lecture: The inverse simulation approach

18
From: Thomson and Bradley, Aeronautical J., 94 (1990). Keynote Lecture: The inverse simulation approach Quick-hop manoeuvre

19
Results are for Lynx helicopter in 300ft quick-hop manoeuvre. Results show the four helicopter control inputs (collective, longitudinal cyclic, lateral cyclic and tail rotor) from flight data and from inverse simulation model. Inverse simulation correctly predicts overall control strategy used by the pilot. But there are differences: are these due to parametric discrepancies in the model or are there issues with the model structure, or both? Could apply qualitative approaches based on parameter sensitivity analysis to gain more insight. Keynote Lecture: The inverse simulation approach Control inputs for quick-hop manoeuvre Figure from: Bradley, Padfield, Murray-Smith and Thomson, Trans. Inst. Measurement and Control, 12(4), 1990.

20
Inverse Simulation for Control Systems ApplicationsInverse Simulation for Control Systems Applications Many applications of model inversion are associated with control system design. Can inverse simulation techniques replace methods of model inversion for control design applications? For example, in combined feed-forward/ feedback model following control systems. Conversely, can techniques and concepts developed in areas such as nonlinear model-based predictive control be used with benefit in inverse simulation methods? Tutorial (4): Control and model validation applications Control systems applications Keynote Lecture: The inverse simulation approach

21
Traditional model-following structure with FFC and FBC The Feedforward (FF) +Feedback (FB) Structure Keynote Lecture: The inverse simulation approach

22
Features of the FF+FB structure Feedforward Channel (FFC): Designed to compensate for the dynamics of the plant. May assist in providing precision tracking. Feedback Channel (FBC): Provides robust stability against uncertainties caused by external disturbances and reduces sensitivity to sensor noise. Reduces the risks of long-term drifts in the overall system response by minimizing the feedforward inaccuracies. Keynote Lecture: The inverse simulation approach

23
. Part 2:A brief review of inverse simulation methods

24
. Keynote Lecture : The inverse simulation approach Inverse modelling/simulation concepts Initial value problem: For inverse solution u(t) has to be found for a given y(t). Differentiating gives: If this equation is solvable for u we can write: The inverse model properties may be significantly different from the original model. Also the forcing function is now dy/dt rather that y(t).

25
Comparisons of model inversion and inverse simulation approaches Model Inversion Highly mathematical basis for nonlinear case. Quite extensively used for aerospace applications. Tends to be rather complex for application to full-scale nonlinear model such as helicopter and ship models. Most approaches only applicable with minimum-phase models. Inverse Simulation Easy and feasible for implementation for minimum-phase systems (more difficult for non-minimum phase systems). Can be applied to many forms of complex nonlinear model without difficulty Keynote Lecture : The inverse simulation approach

26
Iterative methods with discretised models The “differentiation approach” (e.g. Kato and Sugiura 1, Thomson 2 ). This involves replacing derivatives with a discrete equivalent and then solving the resulting nonlinear algebraic equations iteratively to fins the necessary inputs. The “integration approach” (e.g. Hess, Gao and Wang 3 ; Rutherford and Thomson 4 ). This involves repeated solution of the initial value problem and gradient or search-based methods to find the inputs needed to achieve a specified set of outputs. “Optimisation-based approaches”: e.g. Celi’s work on helicopter applications 5. 1. Kato, O. and Sugiura, I, AIAA J. Guidance, Control and Dynamics, 9(2), (1986), 2. Thomson, D.G., In Proc. 12 th European Rotorcraft Forum, 1986, Paper 45. 3.Hess, R.A., Gao, C. and Wang, S.H., AIAA J. Guidance, Control and Dynamics, 14(5), (1991), 733-7. 4. Rutherford, S. and Thomson, D.G., Aeronautical J. 100(993), 1996.,5. Celi, R., Optimization-based inverse simulation of a helicopter maneuver, In: Proc. 25 th Eur. Rotorcraft Forum, Paper H12,1999. Keynote Lecture: The inverse simulation approach

27
Numerical IssuesNumerical Issues Algorithms based on numerical differencing can give rise to problems of rounding error. This presents potential difficulties for the differentiation approach. Problems can also arise through errors in the calculation of the Jacobian for gradient-based optimisation. These can affect both the differentiation and integration based approaches. Potential issues of non-uniqueness of solutions. Keynote Lecture: The inverse simulation approach Numerical issues in the iterative approach

28
Results illustrating numerical problems ) Keynote Lecture: The inverse simulation approach From: Rutherford, S. and Thomson, D.G., ‘ Improved methodologies for inverse simulation’, J. of Aircraft, 100, 79-86,(1996) Numerical instability of type observed by Gao and Hess (1993) and by Rutherford and Thomson (1995) in the application of the iterative integration- based type of approach. Thomson and Bradley (2006) suggest modifying the error function (e.g. using acceleration rather than velocity) to overcome this difficulty when it is encountered. Convergence problems associated with calculation of Jacobian elements are also found in many nonlinear problems.

29
. Part 3: Inverse simulation based on continuous system simulation methods Keynote Lecture : The inverse simulation approach

30
Differential Algebraic Equations (DAE) ApproachDifferential Algebraic Equations (DAE) Approach Allows an inverse simulation model to be derived directly from the structure of the conventional continuous system simulation model. METHOD 1: An inverse model of a Differential Algebraic Equation (DAE) may be constructed simply by changing the meaning of variables. Tools such as Modelica/Dymola or Scilab/Scicos incorporate DAE solver algorithms. METHOD 2: An inverse model can be derived using an approximate continuous differentiator - and an inverse solution can then be found using a continuous equivalent of the discrete “differentiation” approach (Kato and Saguira/Thomson and Bradley) outlined previously. METHOD 3: A continuous systems type description for an inverse model can be constructed through the properties of high gain feedback systems. This is the approach considered in detail in this presentation. Methods based on use of Continuous System Simulation Tools Keynote Lecture: The inverse simulation approach

31
Differential Algebraic Equations (DAE) ApproachDifferential Algebraic Equations (DAE) Approach Allows an inverse simulation model to be derived directly from the structure of the conventional system simulation model. Readily available for users of simulation and modelling tools such as Modelica/Dymola or Scilab/Scicos that incorporate DAE solvers. An inverse model of a DAE is constructed simply by changing the meaning of variables. The result is still a DAE which can be dealt with using standard DAE solution methods. Published results to date are for cases involving relatively simple simulation models. See (or example)Thümmel, M., Looye, G., et al. “Nonlinear inverse models for control”, Proceedings 4 th Intl. Modelica Conference, Hamburg, March 7-8, 2005, pp267-279. The Differential Algebraic Equations (DAE) approach Keynote Lecture: The inverse simulation approach

32
This is a continuous simulation equivalent of the discrete “differentiation method”. The basic idea is to re-arrange the model equations so that the inputs of interest appear on the left hand side. Derivatives of state variables on the right hand side can then be approximated using a simple continuous representation based on the use of an integrator block and feedback (provided T is small in terms of the other dynamics of the system being modelled). An approximate differentiation approach. Keynote Lecture: The inverse simulation approach 1/s w(t) v(t) dw/dt

33
Keynote Lecture: The inverse simulation approach Example: a linear state-space model Poles at s = -1, s = -2, s = -3; zeros at s=-0.5 ± j7.0534 Range of frequencies of interest 0 to 30 rad/s.

34
Keynote Lecture: The inverse simulation approach Re-arrangement of equations + For time constant T small compared with the dynamics of the model this new state variable representation closely approximates the original and we get a satisfactory inverse solution. for the first state equation and a new output equation We now have Let the desired output be defined by The derivative Is then approximated by + +

35
Keynote Lecture: The inverse simulation approach The equations for the inverse simulation + + Zeros at s = -1, s = -2, s = -3. Poles at s=-0.5 ± j7.0534 and at s= -1/T. Original (forward) model had poles at s = -1, s = -2, s = -3. Zeros at s=-0.5 ± j7.0534.

36
The linear state-space model results Model output generated from input found by inverse simulation Input time history found from inverse simulation Keynote Lecture: The inverse simulation approach

37
Based on principles of analogue dividers and inverse function generators and also ideas applied and published by engineers at DLR in Germany in the 1990s (e.g., Hamel (1994) andGray and von Grünhagen (1998)). Quite separately similar ideas ( “inverse dynamics compensation via simulation of feedback control systems” (IDCS))appeared in Japan in the 1990s through the work of Tagawa and Fukui. These methods use high gain feedback principles to generate inputs required to produce a model output that matches a given required output. A feedback systems approach to inverse simulation. Keynote Lecture: The inverse simulation approach

38
Model Feedback gain factor Reference input : required output of model Model input needed to produce require output from the model + _ The feedback approach: the basic idea Keynote Lecture: The inverse simulation approach

39
G(s)G(s) K v w + - For the case where K is very large this gives: The feedback method for the linear case Keynote Lecture: The inverse simulation approach

40
Brief History of the Feedback System ApproachBrief History of the Feedback System Approach Can be traced to feedback methods in analogue computing. The extension to inverse simulation is hard to pin down exactly but much early work was undertaken at the DLR aerospace laboratories at Braunschweig in Germany (Hamel, von Grűnhagen and colleagues). Mainly concerned with aircraft and helicopter flight control. There was independent work in Japan on an approach termed “inverse dynamics compensation via simulation of feedback control systems”. Initially concerned with robotics applications, mainly. (Tagawa and colleagues). More recent work has attempted to generalise the approach and consider a broader range of applications (Murray-Smith) Keynote Lecture: The inverse simulation approach

41
Points to be noted: i G(s)G(s) K v w + - 1)The variable v in this representation is the form of output required from the model while w is input that must be applied to the model (under open-loop conditions) to produce this output. 2) Number of poles of closed-loop system always same as number of zeros and there is no issue of realisability for the inverse. 3) Feedback system design for inverse simulation is distinctly different from control system design – no external disturbances, no issues of robustness. The model G is known exactly - hence high gain solutions and other relatively simple methods of feedback system design (e.g. eigenstructure assignment) can be applied successfully. Keynote Lecture: The inverse simulation approach

42
The linear state-space model again Poles at s = -1, s = -2, s = -3; zeros at s=-0.5 ± j7.0534 Range of frequencies of interest 0 to 30 rad/s. Keynote Lecture: The inverse simulation approach

43
The linear state-space example (continued) Proportional feedback control applied to model using gain factor of 1000 gives closed-loop poles at s = -1005, s = -0.5 ± j7.0 and transmission zeros at s = -3.000, s = -2.000 and s = -1.000 From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. And Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

44
The linear state-space example (continued) Input time history found from inverse simulation showing oscillations that are associated with complex poles in inverse model. Model output generated from the input time history found by inverse simulation. From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. And Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

45
The linear state-space example (continued) Bode diagram for combined system involving model and the inverse simulation in cascade From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. And Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

46
. Part 4: Experience with applications Keynote Lecture: The inverse simulation approach

47
Case study: Model of a 2-tank system A 1 dH 1 /dt = Q i1 – C d1 a 1 [2g (H 1 – H 2 )] ½ A 2 dH 2 /dt = Q i2 + C d1 a 1 [2g (H 1 – H 2 )] ½ - C d2 a 2 [2g (H 2 – H 3 )] ½ Tutorial (3): The feedback approach Analysis of the linearised version of this model shows that it has two poles and no zeros. Laboratory system (Tequipment Ltd) intended for control system experiments. Keynote Lecture: The inverse simulation approach

48
A two-input two-output version of the system Keynote Lecture: The inverse simulation approach

49
Test of inv. sim. by feedback method: input, simulation, inverse and then comparison Keynote Lecture: The inverse simulation approach

50
Inverse simulation involving a required pattern of output levels Keynote Lecture: The inverse simulation approach

51
Inverse simulation by the approximate differentiation method Keynote Lecture: The inverse simulation approach

52
Inverse simulation by feedback with required output levels and input limiting Keynote Lecture: The inverse simulation approach

53
Inverse simulation by differentiation with required output levels and input limiting As compared with the previous case using the feedback approach: Keynote Lecture: The inverse simulation approach

54
Both the approximate differentiation and feedback methods give very similar results for cases where there are no limits. Both are approximate inversion techniques. The two approaches give different results when limits are encountered. Which method you apply must depend on the objectives of the investigation. With the feedback method there are obvious issues of non- uniqueness of solutions (depend on feedback design method used). Similarly, with the differentiation approach the result depends on the magnitude of the time constant in the differentiation loop. Comments on this case study Keynote Lecture: The inverse simulation approach

55
Case Study: Fixed-wing aircraft model using the feedback approach Keynote Lecture: The inverse simulation approach

56
Fixed-wing aircraft example (continued) Analysis of linearised model: Root locus for the feedback system using pitch rate with proportional control and gain factor of 1000 From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. And Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

57
Fixed-wing aircraft example (continued) From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. And Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

58
Fixed-wing aircraft example (continued) Root locus plot involving pitch angle feedback pathway and proportional control Results from inverse simulation with pitch attitude feedback From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. and Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

59
Fixed-wing aircraft example (continued) Results for state-variable feedback from pitch angle, pitch rate, vertical velocity and forward velocity. Poles of closed-loop system lie at or very close to zeros of model. All other poles of closed-loop system lie far from these points. Effect of an elevator deflection l Iimit of ±0.02 rad. Required pitch shown by dashed line. Achievable pitch with this limit shown by continuous line. From Murray-Smith, D. J. Feedback methods for inverse simulation of dynamic models for engineering systems applications, Math. and Computer Modelling of Dynamical Systems,, 17(5), 515-541, 2011 Keynote Lecture: The inverse simulation approach

60
Issues of stability can arise with the feedback approach (both model stability and numerical stability). Obvious issues of non-uniqueness of solutions (depend on feedback design method used or form of approximate differentiator). Feedback analysis and design for inverse simulation is more straightforward than for closed-loop control systems (no issues of measurement noise, robustness or disturbance rejection performance). Computation time with continuous system simulation methods can be less than with other approaches. Comments on these case studies Keynote Lecture: The inverse simulation approach

61
. Part 5: Discussion Keynote Lecture: The inverse simulation approach

62
Ideal Manoeuvre or Expected Control Trajectory Inverse Simulation Algorithms Required Inputs Iterative discrete methods A summary of inverse simulation methods Feedback method Optimisation based methods Approximate differentiation method DAE based methods Keynote Lecture: The inverse simulation approach

63
. Difficulties and limitations Some inherent difficulties and limitations apply to all the approaches in terms of numbers of inputs and outputs etc. These are well covered in the literature. There are fundamental problems with non-minimum-phase systems (and nonlinear equivalents). Inversion of a non-minimum-phase model leads to an unstable inverse model. Differentiation method has difficulties since inversion process involves model manipulation each time the model structure is changed. The feedback approach avoids that but feedback gains may need adjustment. The feedback approach has potential problems of instability and limit cycle phenomena. These problems are most evident when actuator rate limits are present – can be understood using describing function methods. Keynote Lecture: The inverse simulation approach

64
Areas of current research on inverse simulation at the University of Glasgow Further investigation and comparisons in terms of numerical issues for the various different methods of inverse simulation. Further development of inverse simulation methods based on the continuous system simulation approaches. Emphasis is on providing inverse solutions for real-time applications such as those arising in control applications. Application of inverse simulation methods to simulation model validation. Extending previous work involving two-tank process system as well as work involving helicopter flight test data. Use of inverse simulation to investigate actuator rate and amplitude limits for aircraft and underwater vehicle applications. Keynote Lecture: The inverse simulation approach

65
. Conclusions Although it involves approximations inverse simulation provides a potentially useful approach to model inversion that avoids mathematical complexities. Continuous system simulation methods offer alternatives to iterative methods based on discrete models. They avoid known problems of these iterative approaches (e.g. difficulties in determining elements of the Jacobian and associated issues of non-convergence) The approximate differentiation and feedback approaches have both been shown here to be useful for a number of relatively simple engineering applications. Their use with more complex applications (such as a high-order nonlinear UUV model and a nonlinear AUV model) have also proved successful. The two approaches considered have their own strengths and weaknesses. With care, they can offer important physical insight for complex nonlinear analysis and design problems, especially for vehicle systems and control. Keynote Lecture: The inverse simulation approach

66
e I wish to thank Dr Douglas Thomson of the University of Glasgow who was responsible for much of the fundamental research on inverse simulation methods for helicopter applications and has been an active collaborator in a number of applications projects, including some reported in this lecture. I wish also to thank my colleague Dr Euan McGookin of the University of Glasgow who has stimulated my interest in the application of inverse simulation methods to actuator problems associated with the control of ships and underwater vehicles. I wish to acknowledge the support of the US Office of Naval Research in terms of funding to Dr Thomson, Dr McGookin and myself which (among other things) supported my research on the continuous systems simulation approaches to inverse simulation over the period 2009-11. That funding involved awards to California State University, Chico (CSUC) and associated sub-contracts placed at the University of Glasgow by CSUC. A Acknowledgements Keynote Lecture: The inverse simulation approach

Similar presentations

OK

20/10/2009 IVR Herrmann IVR: Introduction to Control OVERVIEW Control systems Transformations Simple control algorithms.

20/10/2009 IVR Herrmann IVR: Introduction to Control OVERVIEW Control systems Transformations Simple control algorithms.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google